One of the common criticisms of the neural network is its lack of interpretability. Why not just show the human brain model and the fact that layer 6 detects faces? Or get intuitions for other tasks? “It works!” does not really satisfy our scientific curiosity. Instead, we’d like to see more data and more insight into the learning process. So, why is the neural network a black box? Let’s examine the reasons.
Essentially, a neural network processes data through a hierarchy of interconnected layers. Much like the biological circuitry of the brain, it is made up of “neurons” that “fire” in response to features of the input data. The first layer processes the raw data input, triggers neurons in the next layer, and passes the signal up to the next level. From there, the network distills the activity of the brain into a single prediction.
Researchers have developed a way to account for the decisions of neural networks by training them to recognize various concepts. By training the neural network to recognize certain concepts, they can show us how much of them it uses to decipher images. A good example of this is an image of a library. The neural network can decipher the different books in the library by their heads, or they can classify cats based on their general shapes.
While the CW is a great way to understand the workings of the brain, it’s also a black box for human understanding. Because the neural network is a black box, there are a number of problems that go unaddressed. Here are some of them:
The main problem with neural networks is that they cannot understand their internal structure, meaning that they can’t interpret the knowledge they learned during the learning process. In fact, the most common reason why neural networks are black boxes is because the learning process is recurrent. A recurrent neural network has multiple copies of the same process, which allows it to retain knowledge and continue learning cycles over again. Moreover, the network’s interfaces are decoupled, which allows the networks to communicate with each other.
One reason why the neural network is a black box is the fact that it cannot generate a statistically identifiable model. The weights of the neural network components may be different, which complicates understanding. One example of a non-black-box model is the generalized regression model. This model generates an interpretable model with reproducible regression coefficients. Furthermore, the model also contains a closed-form function (f) that reflects the importance of each predictor.
Another reason why neural networks are black boxes is that it is difficult to inspect or examine them. The process by which they learn and process information is a mystery. While hand-crafted pipelines build things specific to their environment, neural networks do not. It’s not easy to understand what they have learned. This is because the information encoded in them is so abstract. However, that doesn’t stop us from using them to solve our daily problems.
The complex nature of neural networks makes them unintuitive. Even the simplest neural network with a single hidden layer is difficult to interpret. However, our methodology allows us to understand these networks by exposing the key features that drive each hidden node. For example, by forcing hidden nodes to have sparse connections, we can make the neural network more interpretable. These tradeoffs can be significant, but they are often worth it in the end.
Another issue with these black boxes is their complexity. Because of this, we must be careful not to use them for high-stakes decisions. Often, they may be more complicated than we’d like, but this fact should not discourage us from developing our own algorithms. In fact, open-sourcing algorithms can help us understand the problems associated with black-box models. The truth is that they are often used in many areas of our society, including criminal justice and healthcare.
Another concern with DNNs is their lack of interpretability. Because they contain no heuristic meaning, they are often more vulnerable to noise and adversarial attacks than other types of models. Even when they are applied to data that doesn’t fit in any statistical distribution, a well-performing classifier for depression may not be as accurate as it should be. This is where appropriate interpretation is important to researchers and clinicians.
Another problem with artificial neural networks is their inability to display the learning process. In fact, the user cannot explain how it learned. This is a common pitfall of artificial neural networks. Several researchers have tried to open the black box using information visualization to show the interdependencies between input data and output. This is one way to solve the problem of opaque artificial intelligence. So, we must consider all of these options if we’re serious about using them.